Hen Egg Production Forecasting: Capabilities of Machine Learning Models in Scenarios with Limited Data Sets

To achieve a sophisticated and self-sufficient production environment that aims to optimize a particular production sequence or direction, a combination of multiple interconnected IoT devices, software, and decision-making expertise is required. This is nowadays referred to as "smart" systems and can be related to almost any field. In the case of the poultry industry, "smart" stands for automatic data gathering, in-depth processing, and decision-making support. The implementation of a smart poultry concept introduces several challenges that are production related (e.g., productivity forecasting); therefore, this study focuses on hen egg production forecasting with limited data sets. Different methods and approaches used in the poultry sector for egg production forecasting were investigated. A cross-comparison was made between different models in order to determine their applicability. The models considered include a non-linear Modified Compartmental and several machine learning (ML) models, such as, Long Short-Term Memory (LSTM), Convolutional Neural Network (CNN), XGBoost, and Random Forest (RF). Selected models used only two data sets-one for training and one for testing. Furthermore, the testing data set was significantly different than the training data, therefore setting the forecasting task to be even more challenging. The ML models had significantly more inputs that allowed them to adapt more flexibly to a changing environment in comparison with the nonlinear model that expected only one input, e.g., the week of egg production. The tests showed that the machine learning models proved to be overall more accurate than the selected nonlinear model.